Verified email-pattern data for Tuples Trustworthy Ai is currently limited. You can still use the company insights and contact sections below.
TUPLES (TrUstworthy Planning and scheduling with Learning and ExplanationS) is a 3 year project aiming to obtain scalable, yet transparent, robust and safe algorithmic solutions for P&S.
It will contribute to a more integrated and human-centered approach to the development of P&S tools, in order to increase confidence in these systems and accelerate their adoption.
The cornerstones of our scientific contributions will be:
- combining symbolic P&S methods with data-driven methods to benefit from the scalability and modelling power of the latter, while gaining the transparency, robustness, and safety of the former;
- developing rigorous explanations and verification approaches for ensuring the transparency, robustness, and safety of a sequence of interacting machine learned decisions. Both of these challenges are at the forefront of AI research.
We will demonstrate and evaluate our novel and rigorous methods in a laboratory environment, on a range of use-cases in manufacturing, aircraft operations, sport management, waste collection, and energy management.
OUTCOME 1
To develop hybrid planning and scheduling methods that combine the efficiency, flexibility, and adaptability of data-driven learning approaches with the robustness, reliability, and clarity of model-based reasoning methods.
OUTCOME 2:
To develop verification and explanation methods capable of reasoning about the properties of the solutions produced by planning and scheduling systems, in particular when these are represented by neural networks.
OUTCOME 3:
To demonstrate these approaches on real practical case studies, from airplane pilot assistance, to soccer team. recruitment, and waste collection.
Funded by the European Union under grant agreement No 101070149.
The views expressed are those of the authors and do not necessarily reflect those of the European Union and therefore the latter cannot be held responsible for them.
Company Details
- Employees
- 2
- Founded
- -
- Address
- Offered A Symbolic Backdrop For A Project That Aims To Blend Cutting-Edge Ai Research With Grounded
- Industry
- Research Services
- Website
- https://www.tuples.ai
Tuples Trustworthy Ai Questions
TUPLES Trustworthy AI's website is https://www.tuples.ai
TUPLES Trustworthy AI's LinkedIn profile is https://www.linkedin.com/company/tuplesai
TUPLES Trustworthy AI has
2 employees.
View email and phone details for 2
employees at TUPLES Trustworthy AI.
TUPLES Trustworthy AI's industry is
Research Services
TUPLES Trustworthy AI's top competitors are
Evenflow Project,
Tuples,
Safexplain,
Relax Doctoral Network,
Airbus Aircraft,
Scisports,
Skale2ct | Interreg Europe Project,
Dtai,
Hiverge,
Talon Project.
TUPLES Trustworthy AI's categories are Research Services
TUPLES Trustworthy AI's founding year is 2022
Explore related pages
Related company profiles:
Companies like TUPLES Trustworthy AI
Top TUPLES Trustworthy AI Employees
Free Chrome Extension
Find emails, phones & company data instantly
Find verified emails from LinkedIn profiles
Get direct phone numbers & mobile contacts
Access company data & employee information
Works directly on LinkedIn - no copy/paste needed
Aero Online
Your AI prospecting assistant
Select data to include:
Total price:
$0.00
0 records × $0.02 per record
How It Works
Get a Free Account
Sign up for a free account. No credit card required. Up to 10 free credits.
Search the #1 Contact Database
Get contact details of over 750M+ profiles across 60M companies – all with industry-leading accuracy. Sales Navigator and Recruiter users, try out our Email Finder Extension.
Use our AI-Powered Email Finder
Find business and personal emails and mobile phone numbers with exclusive coverage across niche job titles, industries, and more for unparalleled targeting. Also available via our Contact Data API.